code: def _sparse_conditional(
      Xnew: tf.Tensor,
      inducing_variable: InducingVariables,
      kernel: Kernel,
      f: tf.Tensor,
      *,
      full_cov: bool = False,
      full_output_cov: bool = False,
      q_sqrt: Optional[tf.Tensor] = None,
      white: bool = False,
  ) -> MeanAndVariance:
      """
      Single-output GP conditional.
  
      The covariance matrices used to calculate the conditional have the following shape:
  
      - Kuu: [M, M]
      - Kuf: [M, N]
      - Kff: [N, N]
  
      Further reference:
  
      - See `gpflow.conditionals._dense_conditional` (below) for a detailed explanation of
        conditional in the single-output case.
      - See the multiouput notebook for more information about the multiouput framework.
  
      :param Xnew: data matrix
      :param f: data matrix
      :param full_cov: return the covariance between the datapoints
      :param full_output_cov: return the covariance between the outputs.
          NOTE: as we are using a single-output kernel with repetitions
          these covariances will be zero.
      :param q_sqrt: matrix of standard-deviations or Cholesky matrices,
          size [M, R] or [R, M, M].
      :param white: boolean of whether to use the whitened representation
      :return: mean and variance
      """
      posterior_class = get_posterior_class(kernel, inducing_variable)
  
      posterior: BasePosterior = posterior_class(
          kernel,
          inducing_variable,
          f,
          q_sqrt,
          whiten=white,
          mean_function=None,
          precompute_cache=None,
      )
      return posterior.fused_predict_f(
          Xnew, full_cov=full_cov, full_output_cov=full_output_cov
      )
  
file_path: GPflow/gpflow/conditionals/conditionals.py
imports: from typing import Optional
  import tensorflow
  from check_shapes import check_shapes
  from ..base import MeanAndVariance
  from ..inducing_variables import InducingVariables
  from ..kernels import Kernel
  from ..posteriors import BasePosterior, VGPPosterior, get_posterior_class
  from .dispatch import conditional
label_desc: Use image node_id 1 for calling a global function with example usage: _sparse_conditional(Xnew, inducing_variable, kernel, f) and returns: posterior